The Internet and technology have evolved significantly over past decades. With the speedy development of the internet, applications have grown rapidly such as search engines, blogs, social networking websites, e-commerce websites, etc.
In these applications, social networking websites have become more and more popular. These websites enable users to create a profile of their personal information, keep in touch with their friends and even meet new people with similar interests. Some of the social websites are dating websites which users join with the goal of finding suitable persons to date.
Dating websites typically contain large numbers of members and member data in regards to which matching and searching is necessary to aid the users in finding suitable persons to date. In attempts to solve the problem, search methods have been created, one of which is disclosed in U.S. Pat. No. 7,657,493. However, these search methods are primarily based on preset narrative search conditions, or non-visual personal attributes, like age, interests, location, salary, etc. While sorting for common interests, educational background, age, salary and other such criteria is a simple database storage and search function, these methods do not provide search options regarding visual attributes such as physical attractiveness.
In the area of e-commerce, the structure of e-commerce websites have become more and more complex rendering it difficult for consumers to find the products and service they desire. To address this problem, recommendation methods have been proposed that suggest products and that provide consumers with information to help them decide which products to purchase. Such a recommendation method is disclosed in U.S. Pat. No. 6,370,513.
Known recommendation methods in e-commerce identify relationships between different products based upon, for example, customer purchase history. In dating sites, the subjects of the selection process are human beings not products, for which history would typically be unavailable.
Conventional e-commerce recommendation methods are not capable of meaningfully addressing the difficulty within dating websites of identifying another member who is attractive to the user of the site.
The face is one of the most important and distinctive features of a human being. To locate the similar faces between an input image and each image in a database of faces, some general face recognition methods are used, one of which is disclosed in U.S. Pat. No. 7,430,315.
Existing face recognition methods typically only recognize a face and find the similarity to other face images. However, they do not maximize the real behavioral and emotional components of what a user may find attractive or unattractive among the faces throughout the members in the dating website. It is important to note, that similarity is not the same as attraction. Saying a picture is similar to another picture that you like does not measure how much you like the original picture versus the similar picture, it only states that the images are within a range of closeness to being the same or being identical. A user stating he or she likes an image and that another image is not similar to the image the user likes is in no way saying the user does not like the non-similar image, it is only saying that the images are not similar. In fact, the user may also like the non-similar image. This misconception, that because faces are similar the user is likely to be attracted to both, or because faces are dissimilar the user is likely to be attracted to just one and not the other, is an area where current computer face selection methods miss the mark when it comes to dating websites.
It is difficult to find people to whom an individual user is attracted by appearance, especially among the large number of members on dating websites. Manual searches are time consuming and impractical. In attempts to solve the problem, face similarity search methods have been created such as the one disclosed in U.S. Pat. No. 7,907,755. However, as with traditional face recognition methods, in this disclosed computerized method a single face image a user selects is compared to other face images to find those most similar to that one image. These results are limited to faces having similar facial characteristics to the single face selected by the user. For example, if a user selects an image of Jennifer Aniston, the user will be presented similar faces to Jennifer Aniston, and not faces similar to the multiple other face types the user may also find attractive. A problem which the instant invention overcomes, is that the aforementioned method starts with a user's pre-identified notion of one or more individuals selected for similarity, none of which may be similar to face images within the database of members which the user might nevertheless find attractive. In other words, the user may request the system find similar faces to a single query face of Jennifer Aniston, but there may not be any faces that are similar to Jennifer Aniston's in the members' database, but the members' database may in actuality contain many different face types that the user may find equally attractive but that are not similar to Jennifer Aniston.
Further according to the aforementioned method, the approach of allowing additional user selection criteria only adds another narrow search criteria to the process. For example, allowing the user to request similarity for parts of the face, e.g., eyes, nose, mouth, etc., to be searched individually from the whole face makes for a non-comprehensive approach that, like the initial approach of locating similar faces, fails to account for the emotional and subjective manner in which individuals evaluate the attractiveness of others. This conscious partitioning of individual parts of the face (e.g. eyes, nose, mouth, etc.) demonstrates a clear lack of understanding of how the human subconscious perceives faces as attractive. To one user, the same mouth may be found attractive on one face but unattractive on another. Consciously singling out facial parts does not assist in predicting whether a user will find another given face attractive.
In addition to a user having faces he or she finds attractive, the user will also have faces he or she finds unattractive. Ignoring the unattractive faces creates its own set of limitations that have yet to be addressed by known face selection methods.
The cellular processes of the brain that respond emotionally to a face are subconscious processes which are extremely complex and which respond to the face as a whole, not as a sum of individual face parts. Attraction is an emotional response that is specific to each individual based on each individual's lifetime of experiences.
When using dating sites, finding face types to which a user is attracted but which cannot be identified as words in a profile is often most important to guide users in finding their potential matches among members. Much useful information that is hidden in people's subconscious perception of another's face photograph is not used in the website's search and/or match process, and therefore lost, in conventional face selection methods.
Just like the internet and technology's speedy evolution, our understanding of how the human mind functions has evolved. For example, research indicates the mental process of being attracted or not-attracted to a unique face is a combination of visual recognition followed by emotional response. These two processes occur in separate regions of the brain. A face is seen and recognized as a unique face, then a separate mental process reacts to that face emotionally. Hari, Riitta, Miiamaaria, V. Kujala. Brain Basis of Human Social Interaction: From Concepts to Brain Imaging Physiol Rev April 2009 vol. 89 no. 2 453-479. We are able to consciously identify that we see a face, and describe the face we see, but why we have the emotional reaction that follows the recognition is a subconscious, complex process based on a lifetime of experiences starting from birth. Unlike products where the details as to why we like one over another are easy to put in words, the details as to why one face attracts us and another similar face does not is most often a feeling we recognize but cannot explain with words. In conventional recommendation methods, enjoyable and appealing products are recommended. An organizing method in the context of dating sites which can largely reduce search scopes for users is extremely important. But with limited understanding of how the human brain processes face types and emotional response, current vision dating recommendation methods and e-commerce search methods fall short.
The method of the subject invention is dependent on the involvement of the individual user, and the individual user's assessment of a face or faces as attractive or unattractive, and does not in any way rely, as do some of the prior art, on the assessment of any other person regarding face similarity. So, for example, there are no “training users,” or “human assessors”, i.e., no human agents who perform an identifying and scoring function with respect to multiple photographs in order to provide a basis for identifying faces as similar. Rather, the determination of importance in the context of the instant invention is based solely on the individual user's selections of attractive and unattractive, and not based on any other person's assessments whatsoever.